Manejo de datos en R

Elena Quintero

13/01/2025

Carpeta con material

https://rstats-courses.github.io/CursoR-AEET-2025/materiales.html

Exploración de datos

La exploración de datos nos permite verificar su calidad, generar y probar hipótesis de forma rápida, identificando pistas prometedoras para analizar más a fondo luego.

La visualización de los datos es un buen comienzo, pero por sí sola no suele ser suficiente, ya que a menudo requiere transformar los datos previamente.

https://r4ds.had.co.nz/explore-intro.html

Formato tidy data

Cada variable tiene su propia columna
Cada observación tiene su propia fila
Cada valor tiene su propia celda

R for Data Science - tidy data

Paquetes que usaremos

install.packages(c("tidyverse", 
                   "here",
                   "tidylog", 
                   "summarytools"))

Paquetes incluidos en tidyverse

library(readr)      # leer archivos
library(readxl)     # leer archivos excel
library(dplyr)      # manejar datos
library(tidyr)      # ordenar y trasformar datasets 
library(stringr)    # manejar caracteres
library(forcats)    # manejar factores
library(lubridate)  # manejar fechas

tidyverse::tidyverse_packages()

 [1] "broom"         "conflicted"    "cli"           "dbplyr"       
 [5] "dplyr"         "dtplyr"        "forcats"       "ggplot2"      
 [9] "googledrive"   "googlesheets4" "haven"         "hms"          
[13] "httr"          "jsonlite"      "lubridate"     "magrittr"     
[17] "modelr"        "pillar"        "purrr"         "ragg"         
[21] "readr"         "readxl"        "reprex"        "rlang"        
[25] "rstudioapi"    "rvest"         "stringr"       "tibble"       
[29] "tidyr"         "xml2"          "tidyverse"

Otros paquetes útiles para el manejo de datos

library(tidylog)

Da información de las operaciones que se realizan en el dataset

library(summarytools)

Permite hacer resumenes completos de los datasets

Importar datos

library(base)

read.table(), read.csv(), readRDS(), read.txt()

Argumentos útiles: sep, dec, comment.char, na.strings, stringsAsFactors

library(readr)

read_csv(), read_csv2(), read_table(), read_delim()

Más rapido, produce “tibbles”, no convierte characteres a factors automaticamente, no usa los nombres de fila.

Argumentos útiles: delim, comment, na, col_types, skip_empty_rows, guess_max

library(readxl)

read_excel(), read_xls(), read_xlsx()

Argumentos útiles: sheet, col_types, skip

Ruta a los datos

library(here)

La función here() permite hacer referencia siempre al directorio donde se encuentra el proyecto

Ejemplo usando ruta absoluta:

data <- read_csv("C:/Usuarios/Elena/Documentos/Mis_proyectos/US/Proyecto_frutos/datos/medida_frutos.csv")

Ejemplo usando ruta relativa al proyecto:

data <- read_csv(here("datos/medida_frutos.csv"))

El operador ‘pipe’

Mecanismo para encadenar funciones:

data |> function(...)
  
data %>% function(...)

Dataset

https://doi.org/10.1002/ecy.4463

Dataset

https://doi.org/10.1002/ecy.4463

Cargar paquetes

library(here)
library(tidyverse)
library(tidylog)
library(summarytools)

Importar datos

dt_raw <- read_csv(here("data/individual_seed_production.csv"))

Rows: 213062 Columns: 14
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (8): site_name, megaplot, plot, plant_ID, species_name, height_diameter_...
dbl (6): trap, year, count, stem_diameter_cm, trap_area_m2, burned

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

glimpse(dt_raw)

Rows: 213,062
Columns: 14
$ site_name             <chr> "AND", "AND", "AND", "AND", "AND", "AND", "AND",…
$ megaplot              <chr> "Bare Mountain", "Bare Mountain", "Bare Mountain…
$ plot                  <chr> "CNCT_01", "CNCT_01", "CNCT_01", "CNCT_01", "CNC…
$ trap                  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ plant_ID              <chr> "CNCT_01ABAM1", "CNCT_01ABAM1", "CNCT_01ABAM1", …
$ species_name          <chr> "Abies_amabilis", "Abies_amabilis", "Abies_amabi…
$ year                  <dbl> 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, …
$ count                 <dbl> 22, 0, 0, 2, 0, 2, 108, 0, 0, 7, 0, 0, 2, 0, 12,…
$ stem_diameter_cm      <dbl> 56.6, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ trap_area_m2          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ height_diameter_taken <chr> "Breast Height", "Breast Height", "Breast Height…
$ burned                <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ general_method        <chr> "PARTIALCONECOUNT", "PARTIALCONECOUNT", "PARTIAL…
$ methods_notes         <chr> "all cones visible from established view points …

head(dt_raw)

# A tibble: 6 × 14
  site_name megaplot      plot     trap plant_ID     species_name    year count
  <chr>     <chr>         <chr>   <dbl> <chr>        <chr>          <dbl> <dbl>
1 AND       Bare Mountain CNCT_01    NA CNCT_01ABAM1 Abies_amabilis  1962    22
2 AND       Bare Mountain CNCT_01    NA CNCT_01ABAM1 Abies_amabilis  1963     0
3 AND       Bare Mountain CNCT_01    NA CNCT_01ABAM1 Abies_amabilis  1964     0
4 AND       Bare Mountain CNCT_01    NA CNCT_01ABAM1 Abies_amabilis  1965     2
5 AND       Bare Mountain CNCT_01    NA CNCT_01ABAM1 Abies_amabilis  1966     0
6 AND       Bare Mountain CNCT_01    NA CNCT_01ABAM1 Abies_amabilis  1967     2
# ℹ 6 more variables: stem_diameter_cm <dbl>, trap_area_m2 <dbl>,
#   height_diameter_taken <chr>, burned <dbl>, general_method <chr>,
#   methods_notes <chr>

Funciones de dplyr (parte 1)

arrange() - Ordenar variables por casos
rename() - Renombrar variables
relocate() - Reordenar variables
select() - Extraer variables

Ordernar datos por columnas

dt_raw |> 
  arrange(count)

# A tibble: 213,062 × 14
   site_name megaplot      plot     trap plant_ID     species_name    year count
   <chr>     <chr>         <chr>   <dbl> <chr>        <chr>          <dbl> <dbl>
 1 AND       Bare Mountain CNCT_01    NA CNCT_01ABAM1 Abies_amabilis  1963     0
 2 AND       Bare Mountain CNCT_01    NA CNCT_01ABAM1 Abies_amabilis  1964     0
 3 AND       Bare Mountain CNCT_01    NA CNCT_01ABAM1 Abies_amabilis  1966     0
 4 AND       Bare Mountain CNCT_01    NA CNCT_01ABAM1 Abies_amabilis  1969     0
 5 AND       Bare Mountain CNCT_01    NA CNCT_01ABAM1 Abies_amabilis  1970     0
 6 AND       Bare Mountain CNCT_01    NA CNCT_01ABAM1 Abies_amabilis  1972     0
 7 AND       Bare Mountain CNCT_01    NA CNCT_01ABAM1 Abies_amabilis  1973     0
 8 AND       Bare Mountain CNCT_01    NA CNCT_01ABAM1 Abies_amabilis  1975     0
 9 AND       Bare Mountain CNCT_01    NA CNCT_01ABAM1 Abies_amabilis  1977     0
10 AND       Bare Mountain CNCT_01    NA CNCT_01ABAM1 Abies_amabilis  1981     0
# ℹ 213,052 more rows
# ℹ 6 more variables: stem_diameter_cm <dbl>, trap_area_m2 <dbl>,
#   height_diameter_taken <chr>, burned <dbl>, general_method <chr>,
#   methods_notes <chr>

Ordernar datos por columnas

De mayor a menor:

dt_raw |> 
  arrange(desc(count))

# A tibble: 213,062 × 14
   site_name megaplot plot   trap plant_ID species_name           year   count
   <chr>     <chr>    <chr> <dbl> <chr>    <chr>                 <dbl>   <dbl>
 1 LUQ       1        1        92 <NA>     Cecropia_schreberiana  1997 1114340
 2 LUQ       1        1        93 <NA>     Ficus_trigonata        2015  106650
 3 LUQ       1        1        92 <NA>     Ficus_trigonata        2013   69450
 4 LUQ       1        1        93 <NA>     Ficus_trigonata        2018   44090
 5 LUQ       1        1       109 <NA>     Ficus_trigonata        2015   39670
 6 LUQ       1        1        92 <NA>     Ficus_trigonata        2015   35075
 7 LUQ       1        1        93 <NA>     Ficus_trigonata        2016   33500
 8 LUQ       1        1        92 <NA>     Ficus_trigonata        2008   33000
 9 LUQ       1        1       107 <NA>     Ficus_trigonata        2011   32689
10 LUQ       1        1        99 <NA>     Cecropia_schreberiana  2009   30594
# ℹ 213,052 more rows
# ℹ 6 more variables: stem_diameter_cm <dbl>, trap_area_m2 <dbl>,
#   height_diameter_taken <chr>, burned <dbl>, general_method <chr>,
#   methods_notes <chr>

Ordernar datos por columnas

Por orden jerárquico:

dt_raw |> 
  arrange(site_name, species_name, desc(count))

# A tibble: 213,062 × 14
   site_name megaplot   plot        trap plant_ID species_name  year count
   <chr>     <chr>      <chr>      <dbl> <chr>    <chr>        <dbl> <dbl>
 1 AEC       adirondack adirondack   971 <NA>     Acer_rubrum   2009   191
 2 AEC       adirondack adirondack   971 <NA>     Acer_rubrum   2004   171
 3 AEC       adirondack adirondack   971 <NA>     Acer_rubrum   1995   141
 4 AEC       adirondack adirondack   941 <NA>     Acer_rubrum   1994   105
 5 AEC       adirondack adirondack   941 <NA>     Acer_rubrum   1995    85
 6 AEC       adirondack adirondack   972 <NA>     Acer_rubrum   2007    82
 7 AEC       adirondack adirondack   971 <NA>     Acer_rubrum   2008    81
 8 AEC       adirondack adirondack   971 <NA>     Acer_rubrum   1993    79
 9 AEC       adirondack adirondack   941 <NA>     Acer_rubrum   1991    77
10 AEC       adirondack adirondack   938 <NA>     Acer_rubrum   2004    72
# ℹ 213,052 more rows
# ℹ 6 more variables: stem_diameter_cm <dbl>, trap_area_m2 <dbl>,
#   height_diameter_taken <chr>, burned <dbl>, general_method <chr>,
#   methods_notes <chr>

Renombrar variables

dt_raw |> 
  rename(site = site_name)

rename: renamed one variable (site)

# A tibble: 213,062 × 14
   site  megaplot plot   trap plant_ID species_name  year count stem_diameter_cm
   <chr> <chr>    <chr> <dbl> <chr>    <chr>        <dbl> <dbl>            <dbl>
 1 AND   Bare Mo… CNCT…    NA CNCT_01… Abies_amabi…  1962    22             56.6
 2 AND   Bare Mo… CNCT…    NA CNCT_01… Abies_amabi…  1963     0             NA  
 3 AND   Bare Mo… CNCT…    NA CNCT_01… Abies_amabi…  1964     0             NA  
 4 AND   Bare Mo… CNCT…    NA CNCT_01… Abies_amabi…  1965     2             NA  
 5 AND   Bare Mo… CNCT…    NA CNCT_01… Abies_amabi…  1966     0             NA  
 6 AND   Bare Mo… CNCT…    NA CNCT_01… Abies_amabi…  1967     2             NA  
 7 AND   Bare Mo… CNCT…    NA CNCT_01… Abies_amabi…  1968   108             NA  
 8 AND   Bare Mo… CNCT…    NA CNCT_01… Abies_amabi…  1969     0             NA  
 9 AND   Bare Mo… CNCT…    NA CNCT_01… Abies_amabi…  1970     0             NA  
10 AND   Bare Mo… CNCT…    NA CNCT_01… Abies_amabi…  1971     7             NA  
# ℹ 213,052 more rows
# ℹ 5 more variables: trap_area_m2 <dbl>, height_diameter_taken <chr>,
#   burned <dbl>, general_method <chr>, methods_notes <chr>

Organizar columnas

dt_raw |> 
  relocate(year, .before = megaplot)

relocate: columns reordered (site_name, year, megaplot, plot, trap, …)

# A tibble: 213,062 × 14
   site_name  year megaplot      plot     trap plant_ID     species_name   count
   <chr>     <dbl> <chr>         <chr>   <dbl> <chr>        <chr>          <dbl>
 1 AND        1962 Bare Mountain CNCT_01    NA CNCT_01ABAM1 Abies_amabilis    22
 2 AND        1963 Bare Mountain CNCT_01    NA CNCT_01ABAM1 Abies_amabilis     0
 3 AND        1964 Bare Mountain CNCT_01    NA CNCT_01ABAM1 Abies_amabilis     0
 4 AND        1965 Bare Mountain CNCT_01    NA CNCT_01ABAM1 Abies_amabilis     2
 5 AND        1966 Bare Mountain CNCT_01    NA CNCT_01ABAM1 Abies_amabilis     0
 6 AND        1967 Bare Mountain CNCT_01    NA CNCT_01ABAM1 Abies_amabilis     2
 7 AND        1968 Bare Mountain CNCT_01    NA CNCT_01ABAM1 Abies_amabilis   108
 8 AND        1969 Bare Mountain CNCT_01    NA CNCT_01ABAM1 Abies_amabilis     0
 9 AND        1970 Bare Mountain CNCT_01    NA CNCT_01ABAM1 Abies_amabilis     0
10 AND        1971 Bare Mountain CNCT_01    NA CNCT_01ABAM1 Abies_amabilis     7
# ℹ 213,052 more rows
# ℹ 6 more variables: stem_diameter_cm <dbl>, trap_area_m2 <dbl>,
#   height_diameter_taken <chr>, burned <dbl>, general_method <chr>,
#   methods_notes <chr>

Seleccionar variables de interés

dt_raw |> 
  select(site_name, year, species_name, count)

select: dropped 10 variables (megaplot, plot, trap, plant_ID, stem_diameter_cm, …)

# A tibble: 213,062 × 4
   site_name  year species_name   count
   <chr>     <dbl> <chr>          <dbl>
 1 AND        1962 Abies_amabilis    22
 2 AND        1963 Abies_amabilis     0
 3 AND        1964 Abies_amabilis     0
 4 AND        1965 Abies_amabilis     2
 5 AND        1966 Abies_amabilis     0
 6 AND        1967 Abies_amabilis     2
 7 AND        1968 Abies_amabilis   108
 8 AND        1969 Abies_amabilis     0
 9 AND        1970 Abies_amabilis     0
10 AND        1971 Abies_amabilis     7
# ℹ 213,052 more rows

Seleccionar variables de interés

Quitar variables:

dt_raw |> 
  select(-c(megaplot, plot, trap))

select: dropped 3 variables (megaplot, plot, trap)

# A tibble: 213,062 × 11
   site_name plant_ID     species_name  year count stem_diameter_cm trap_area_m2
   <chr>     <chr>        <chr>        <dbl> <dbl>            <dbl>        <dbl>
 1 AND       CNCT_01ABAM1 Abies_amabi…  1962    22             56.6           NA
 2 AND       CNCT_01ABAM1 Abies_amabi…  1963     0             NA             NA
 3 AND       CNCT_01ABAM1 Abies_amabi…  1964     0             NA             NA
 4 AND       CNCT_01ABAM1 Abies_amabi…  1965     2             NA             NA
 5 AND       CNCT_01ABAM1 Abies_amabi…  1966     0             NA             NA
 6 AND       CNCT_01ABAM1 Abies_amabi…  1967     2             NA             NA
 7 AND       CNCT_01ABAM1 Abies_amabi…  1968   108             NA             NA
 8 AND       CNCT_01ABAM1 Abies_amabi…  1969     0             NA             NA
 9 AND       CNCT_01ABAM1 Abies_amabi…  1970     0             NA             NA
10 AND       CNCT_01ABAM1 Abies_amabi…  1971     7             NA             NA
# ℹ 213,052 more rows
# ℹ 4 more variables: height_diameter_taken <chr>, burned <dbl>,
#   general_method <chr>, methods_notes <chr>

Seleccionar variables de interés

La función select() nos permite seleccionar, renombrar y recolocar - todo a la vez!

dt <- dt_raw |> 
  select(site = site_name,
         year,
         species_name,
         plant_ID,
         count,
         method = general_method,
         stem_cm = stem_diameter_cm,
         trap_area_m2)

select: renamed 3 variables (site, method, stem_cm) and dropped 6 variables

Seleccionar variables de interés

La función select() nos permite seleccionar, renombrar y recolocar - todo a la vez!

glimpse(dt)

Rows: 213,062
Columns: 8
$ site         <chr> "AND", "AND", "AND", "AND", "AND", "AND", "AND", "AND", "…
$ year         <dbl> 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 197…
$ species_name <chr> "Abies_amabilis", "Abies_amabilis", "Abies_amabilis", "Ab…
$ plant_ID     <chr> "CNCT_01ABAM1", "CNCT_01ABAM1", "CNCT_01ABAM1", "CNCT_01A…
$ count        <dbl> 22, 0, 0, 2, 0, 2, 108, 0, 0, 7, 0, 0, 2, 0, 12, 0, 21, 1…
$ method       <chr> "PARTIALCONECOUNT", "PARTIALCONECOUNT", "PARTIALCONECOUNT…
$ stem_cm      <dbl> 56.6, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ trap_area_m2 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…

Resumen datos

summary(dt$species_name)

   Length     Class      Mode 
   213062 character character

Resumen datos

dfSummary(dt$species_name)

Data Frame Summary  
dt  
Dimensions: 213062 x 1  
Duplicates: 212921  

-----------------------------------------------------------------------------------------------------
No   Variable       Stats / Values            Freqs (% of Valid)   Graph         Valid      Missing  
---- -------------- ------------------------- -------------------- ------------- ---------- ---------
1    species_name   1. Abies_amabilis          15488 ( 7.3%)       I             213062     0        
     [character]    2. Juniperus_monosperma    10201 ( 4.8%)                     (100.0%)   (0.0%)   
                    3. Amelanchier_arborea     10182 ( 4.8%)                                         
                    4. Abies_procera           10141 ( 4.8%)                                         
                    5. Tsuga_mertensiana        9684 ( 4.5%)                                         
                    6. Acer_saccharum           8403 ( 3.9%)                                         
                    7. Fraxinus_americana       7997 ( 3.8%)                                         
                    8. Acer_rubrum              6231 ( 2.9%)                                         
                    9. Tsuga_canadensis         6078 ( 2.9%)                                         
                    10. Sassafras_albidum       5200 ( 2.4%)                                         
                    [ 131 others ]            123457 (57.9%)       IIIIIIIIIII                       
-----------------------------------------------------------------------------------------------------

Resumen datos

summary(dt$count)

     Min.   1st Qu.    Median      Mean   3rd Qu.      Max.      NA's 
      0.0       0.0       0.0      34.6       4.0 1114340.0      4015

Resumen datos

dfSummary(dt$count)

Data Frame Summary  
dt  
Dimensions: 213062 x 1  
Duplicates: 211712  

-----------------------------------------------------------------------------------------------
No   Variable    Stats / Values              Freqs (% of Valid)     Graph   Valid     Missing  
---- ----------- --------------------------- ---------------------- ------- --------- ---------
1    count       Mean (sd) : 34.6 (2481.6)   1349 distinct values   :       209047    4015     
     [numeric]   min < med < max:                                   :       (98.1%)   (1.9%)   
                 0 < 0 < 1114340                                    :                          
                 IQR (CV) : 4 (71.7)                                :                          
                                                                    :                          
-----------------------------------------------------------------------------------------------

Funciones de dplyr (parte 2):

distinct() - Extraer valores únicos
mutate() - Crear nuevas variables
filter() - Filtrar datos por casos
group_by() - Agrupar datos por casos
summarise() - Resumir datos por casos
case_when() - Categorizar datos

Extraer valores únicos

Niveles de una variable:

dt |>
  distinct(site)

# A tibble: 9 × 1
  site 
  <chr>
1 AND  
2 SEV  
3 CDR  
4 HFR  
5 AEC  
6 HBR  
7 BNZ  
8 CWT  
9 LUQ

Extraer valores únicos

Equivalente en library(base):

unique(dt$site)

[1] "AND" "SEV" "CDR" "HFR" "AEC" "HBR" "BNZ" "CWT" "LUQ"

Extraer valores únicos

Niveles de una variable:

dt |>
  distinct(site, method)

# A tibble: 10 × 2
   site  method            
   <chr> <chr>             
 1 AND   PARTIALCONECOUNT  
 2 SEV   ESTIMATEDSEEDCOUNT
 3 SEV   CONECOUNT         
 4 CDR   TIMEDSEEDCOUNT    
 5 HFR   TIMEDSEEDCOUNT    
 6 AEC   TRAP              
 7 HBR   TRAP              
 8 BNZ   TRAP              
 9 CWT   TRAP              
10 LUQ   TRAP

Crear nuevas variables

Ej: transformar frutos a frutos/m2

dt |>
  mutate(fruits_per_m2 = count/trap_area_m2)

mutate: new variable 'fruits_per_m2' (double) with 2,116 unique values and 35% NA

# A tibble: 213,062 × 9
   site   year species_name   plant_ID     count method     stem_cm trap_area_m2
   <chr> <dbl> <chr>          <chr>        <dbl> <chr>        <dbl>        <dbl>
 1 AND    1962 Abies_amabilis CNCT_01ABAM1    22 PARTIALCO…    56.6           NA
 2 AND    1963 Abies_amabilis CNCT_01ABAM1     0 PARTIALCO…    NA             NA
 3 AND    1964 Abies_amabilis CNCT_01ABAM1     0 PARTIALCO…    NA             NA
 4 AND    1965 Abies_amabilis CNCT_01ABAM1     2 PARTIALCO…    NA             NA
 5 AND    1966 Abies_amabilis CNCT_01ABAM1     0 PARTIALCO…    NA             NA
 6 AND    1967 Abies_amabilis CNCT_01ABAM1     2 PARTIALCO…    NA             NA
 7 AND    1968 Abies_amabilis CNCT_01ABAM1   108 PARTIALCO…    NA             NA
 8 AND    1969 Abies_amabilis CNCT_01ABAM1     0 PARTIALCO…    NA             NA
 9 AND    1970 Abies_amabilis CNCT_01ABAM1     0 PARTIALCO…    NA             NA
10 AND    1971 Abies_amabilis CNCT_01ABAM1     7 PARTIALCO…    NA             NA
# ℹ 213,052 more rows
# ℹ 1 more variable: fruits_per_m2 <dbl>

Filtrar datos

dt |>
  filter(site == "BNZ")

filter: removed 208,641 rows (98%), 4,421 rows remaining

# A tibble: 4,421 × 8
   site   year species_name plant_ID count method stem_cm trap_area_m2
   <chr> <dbl> <chr>        <chr>    <dbl> <chr>    <dbl>        <dbl>
 1 BNZ    1957 Picea_glauca <NA>         3 TRAP        NA         0.25
 2 BNZ    1957 Picea_glauca <NA>         1 TRAP        NA         0.25
 3 BNZ    1957 Picea_glauca <NA>         2 TRAP        NA         0.25
 4 BNZ    1957 Picea_glauca <NA>         3 TRAP        NA         0.25
 5 BNZ    1957 Picea_glauca <NA>         3 TRAP        NA         0.25
 6 BNZ    1957 Picea_glauca <NA>         0 TRAP        NA         0.25
 7 BNZ    1957 Picea_glauca <NA>         2 TRAP        NA         0.25
 8 BNZ    1957 Picea_glauca <NA>         3 TRAP        NA         0.25
 9 BNZ    1957 Picea_glauca <NA>         0 TRAP        NA         0.25
10 BNZ    1957 Picea_glauca <NA>         1 TRAP        NA         0.25
# ℹ 4,411 more rows

Filtrar datos

dt |>
  filter(site %in% c("AEC", "AND", "BNZ")) |> 
  filter(count >= 10)

filter: removed 150,475 rows (71%), 62,587 rows remaining

filter: removed 41,369 rows (66%), 21,218 rows remaining

# A tibble: 21,218 × 8
   site   year species_name   plant_ID     count method     stem_cm trap_area_m2
   <chr> <dbl> <chr>          <chr>        <dbl> <chr>        <dbl>        <dbl>
 1 AND    1962 Abies_amabilis CNCT_01ABAM1    22 PARTIALCO…    56.6           NA
 2 AND    1968 Abies_amabilis CNCT_01ABAM1   108 PARTIALCO…    NA             NA
 3 AND    1976 Abies_amabilis CNCT_01ABAM1    12 PARTIALCO…    NA             NA
 4 AND    1978 Abies_amabilis CNCT_01ABAM1    21 PARTIALCO…    NA             NA
 5 AND    1980 Abies_amabilis CNCT_01ABAM1    30 PARTIALCO…    NA             NA
 6 AND    1982 Abies_amabilis CNCT_01ABAM1    61 PARTIALCO…    NA             NA
 7 AND    1985 Abies_amabilis CNCT_01ABAM1    76 PARTIALCO…    NA             NA
 8 AND    1991 Abies_amabilis CNCT_01ABAM1    42 PARTIALCO…    NA             NA
 9 AND    1995 Abies_amabilis CNCT_01ABAM1    75 PARTIALCO…    NA             NA
10 AND    1997 Abies_amabilis CNCT_01ABAM1    52 PARTIALCO…    NA             NA
# ℹ 21,208 more rows

Agrupar datos y resumir

dt |>
  group_by(site) |> 
  summarise(fruits = sum(count))

group_by: one grouping variable (site)

summarise: now 9 rows and 2 columns, ungrouped

# A tibble: 9 × 2
  site    fruits
  <chr>    <dbl>
1 AEC     55731.
2 AND        NA 
3 BNZ    915902 
4 CDR     85431 
5 CWT        NA 
6 HBR     24556.
7 HFR      3683 
8 LUQ   3653588 
9 SEV        NA

Agrupar datos y resumir

dt |>
  group_by(site) |> 
  summarise(fruits = sum(count, na.rm = TRUE))

group_by: one grouping variable (site)

summarise: now 9 rows and 2 columns, ungrouped

# A tibble: 9 × 2
  site    fruits
  <chr>    <dbl>
1 AEC     55731.
2 AND   1968048 
3 BNZ    915902 
4 CDR     85431 
5 CWT    292939 
6 HBR     24556.
7 HFR      3683 
8 LUQ   3653588 
9 SEV    231905.

Agrupar datos y resumir

dt |>
  group_by(site) |> 
  summarise(max_fruit = max(count, na.rm = TRUE), 
            min_fruit = min(count, na.rm = TRUE))

group_by: one grouping variable (site)

summarise: now 9 rows and 3 columns, ungrouped

# A tibble: 9 × 3
  site  max_fruit min_fruit
  <chr>     <dbl>     <dbl>
1 AEC         591         0
2 AND        5000         0
3 BNZ        7230         0
4 CDR         151         0
5 CWT        1383         0
6 HBR         244         0
7 HFR          77         0
8 LUQ     1114340         0
9 SEV        1100         0

Agrupar datos y resumir

Crear dataset con media de frutos de cada especie de árbol por sitio y por año:

dt |>
  group_by(site, species_name, year) |>
  summarise(mean_fruits = mean(count, na.rm = TRUE)) |>
  ungroup()

group_by: 3 grouping variables (site, species_name, year)

summarise: now 3,212 rows and 4 columns, 2 group variables remaining (site, species_name)

ungroup: no grouping variables

# A tibble: 3,212 × 4
   site  species_name  year mean_fruits
   <chr> <chr>        <dbl>       <dbl>
 1 AEC   Acer_rubrum   1988        0   
 2 AEC   Acer_rubrum   1989        3.1 
 3 AEC   Acer_rubrum   1990        0.44
 4 AEC   Acer_rubrum   1991        9.36
 5 AEC   Acer_rubrum   1992        3.90
 6 AEC   Acer_rubrum   1993        4.45
 7 AEC   Acer_rubrum   1994        9.75
 8 AEC   Acer_rubrum   1995        6.52
 9 AEC   Acer_rubrum   1996        6.86
10 AEC   Acer_rubrum   1997        1.80
# ℹ 3,202 more rows

Crear categorias

Crear una nueva variable en base a diferentes niveles de frutos.

Ej - un factor de 3 niveles de cantidad frutos:

dt |> 
  filter(!is.na(count)) |> 
  filter(count != 0) |> 
  select(count) |> 
  summary()

filter: removed 4,015 rows (2%), 209,047 rows remaining

filter: removed 126,766 rows (61%), 82,281 rows remaining

select: dropped 7 variables (site, year, species_name, plant_ID, method, …)

     count          
 Min.   :      0.1  
 1st Qu.:      2.0  
 Median :      8.0  
 Mean   :     87.9  
 3rd Qu.:     35.0  
 Max.   :1114340.0

Crear categorias

Crear una nueva variable en base a diferentes niveles de frutos.

Ej - un factor de 3 niveles de cantidad frutos:

dt |>
  mutate(nivel_frutos = case_when( 
    count <= 100 ~ "bajo",
    count > 100 & count <= 1000 ~ "medio",
    count > 1000 ~ "alto"))

mutate: new variable 'nivel_frutos' (character) with 4 unique values and 2% NA

# A tibble: 213,062 × 9
   site   year species_name   plant_ID     count method     stem_cm trap_area_m2
   <chr> <dbl> <chr>          <chr>        <dbl> <chr>        <dbl>        <dbl>
 1 AND    1962 Abies_amabilis CNCT_01ABAM1    22 PARTIALCO…    56.6           NA
 2 AND    1963 Abies_amabilis CNCT_01ABAM1     0 PARTIALCO…    NA             NA
 3 AND    1964 Abies_amabilis CNCT_01ABAM1     0 PARTIALCO…    NA             NA
 4 AND    1965 Abies_amabilis CNCT_01ABAM1     2 PARTIALCO…    NA             NA
 5 AND    1966 Abies_amabilis CNCT_01ABAM1     0 PARTIALCO…    NA             NA
 6 AND    1967 Abies_amabilis CNCT_01ABAM1     2 PARTIALCO…    NA             NA
 7 AND    1968 Abies_amabilis CNCT_01ABAM1   108 PARTIALCO…    NA             NA
 8 AND    1969 Abies_amabilis CNCT_01ABAM1     0 PARTIALCO…    NA             NA
 9 AND    1970 Abies_amabilis CNCT_01ABAM1     0 PARTIALCO…    NA             NA
10 AND    1971 Abies_amabilis CNCT_01ABAM1     7 PARTIALCO…    NA             NA
# ℹ 213,052 more rows
# ℹ 1 more variable: nivel_frutos <chr>

Crear categorias

Contar numero de arboles con distintos niveles de frutos:

dt |>
  mutate(nivel_frutos = case_when( 
    count <= 100 ~ "bajo",
    count > 100 & count <= 1000 ~ "medio",
    count > 1000 ~ "alto")) |> 
  group_by(nivel_frutos) |>
  summarise(trees = n())

mutate: new variable 'nivel_frutos' (character) with 4 unique values and 2% NA

group_by: one grouping variable (nivel_frutos)

summarise: now 4 rows and 2 columns, ungrouped

# A tibble: 4 × 2
  nivel_frutos  trees
  <chr>         <int>
1 alto            698
2 bajo         200157
3 medio          8192
4 <NA>           4015

Funciones vistas de dplyr

Funciones de dplyr (parte 1 y 2)

arrange() - Ordenar variable por casos
rename() - Renombrar variables
relocate() - Reordenar variables
select() - Extraer variables
distinct() - Extraer valores únicos
mutate() - Crear nuevas variables
filter() - Filtrar datos por casos
group_by() - Agrupar datos por casos
summarise() - Resumir datos por casos
case_when() - Filtrar datos por casos

Corregir datos

Función if_else():

dt_fix <- dt |>
  # quitar un valor equivocado
  mutate(count = if_else(count > 200000, NA, count))

mutate: changed one value (<1%) of 'count' (1 new NA)

Modificar datos

Función if_else():

dt_fix <- dt |>
  # quitar un valor equivocado
  mutate(count = if_else(count > 200000, NA, count)) |> 
  # calcular número de frutos por m2
  mutate(fruits_per_m2 = count/trap_area_m2) |> 
  # crear variable con la cantidad de frutos de count o corregida
  mutate(fruits = if_else(is.na(fruits_per_m2), count, fruits_per_m2))

mutate: changed one value (<1%) of 'count' (1 new NA)

mutate: new variable 'fruits_per_m2' (double) with 2,115 unique values and 35% NA

mutate: new variable 'fruits' (double) with 2,305 unique values and 2% NA

Modificar datos

Función if_else():

dt_fix <- dt |>
  # quitar un valor equivocado
  mutate(count = if_else(count > 200000, NA, count)) |> 
  # calcular número de frutos por m2
  mutate(fruits_per_m2 = count/trap_area_m2) |> 
  # crear variable con la cantidad de frutos de count o corregida
  mutate(fruits = if_else(is.na(fruits_per_m2), count, fruits_per_m2)) |> 
  # quitar valores de 0 o NA
  filter(count != 0)

mutate: changed one value (<1%) of 'count' (1 new NA)

mutate: new variable 'fruits_per_m2' (double) with 2,115 unique values and 35% NA

mutate: new variable 'fruits' (double) with 2,305 unique values and 2% NA

filter: removed 130,782 rows (61%), 82,280 rows remaining

Reestructurar datos

Reestructurar datos con `library(tidyr)`

Función pivot_wider()
Función pivot_longer()

Fuente: Garrick Aden-Buie’s - Tidyexplained Verbs

Reestructurar datos

head(dt_fix)

# A tibble: 6 × 10
  site   year species_name   plant_ID     count method      stem_cm trap_area_m2
  <chr> <dbl> <chr>          <chr>        <dbl> <chr>         <dbl>        <dbl>
1 AND    1962 Abies_amabilis CNCT_01ABAM1    22 PARTIALCON…    56.6           NA
2 AND    1965 Abies_amabilis CNCT_01ABAM1     2 PARTIALCON…    NA             NA
3 AND    1967 Abies_amabilis CNCT_01ABAM1     2 PARTIALCON…    NA             NA
4 AND    1968 Abies_amabilis CNCT_01ABAM1   108 PARTIALCON…    NA             NA
5 AND    1971 Abies_amabilis CNCT_01ABAM1     7 PARTIALCON…    NA             NA
6 AND    1974 Abies_amabilis CNCT_01ABAM1     2 PARTIALCON…    NA             NA
# ℹ 2 more variables: fruits_per_m2 <dbl>, fruits <dbl>

Reestructurar datos

Primero creamos dataset reducido:

dt_fix |>
  group_by(site, year) |> 
  summarise(fruits = mean(fruits, na.rm. = TRUE))

group_by: 2 grouping variables (site, year)

summarise: now 280 rows and 3 columns, one group variable remaining (site)

# A tibble: 280 × 3
# Groups:   site [9]
   site   year fruits
   <chr> <dbl>  <dbl>
 1 AEC    1988  252. 
 2 AEC    1989  656. 
 3 AEC    1990   67.3
 4 AEC    1991  148. 
 5 AEC    1992  279. 
 6 AEC    1993   66.1
 7 AEC    1994  375. 
 8 AEC    1995  343. 
 9 AEC    1996  250. 
10 AEC    1997  233. 
# ℹ 270 more rows

Reestructurar datos

Convertir a formato corto:

dt_short <- dt_fix |>
  group_by(site, year) |> 
  summarise(fruits = mean(fruits, na.rm. = TRUE)) |> 
  pivot_wider(names_from = "site",
              values_from = "fruits")

group_by: 2 grouping variables (site, year)

summarise: now 280 rows and 3 columns, one group variable remaining (site)

pivot_wider: reorganized (site, fruits) into (AEC, AND, BNZ, CDR, CWT, …) [was 280x3, now 65x10]

head(dt_short)

# A tibble: 6 × 10
   year   AEC   AND   BNZ   CDR   CWT   HBR   HFR   LUQ   SEV
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1  1988 252.   27.0  198.    NA   NA   NA      NA   NA     NA
2  1989 656.   46.6 1406.    NA   NA   NA      NA   NA     NA
3  1990  67.3  26.2  909.    NA   NA   NA      NA   NA     NA
4  1991 148.  140.  1318.    NA  154.  NA      NA   NA     NA
5  1992 279.   28.4  357.    NA  101.  NA      NA  249.    NA
6  1993  66.1  65.1 1683.    NA  124.  43.9    NA  269.    NA

Reestructurar datos

Convertir a formato largo:

dt_short |>
  pivot_longer(cols = c(AEC:SEV),
               names_to = "site",
               values_to = "fruits")

pivot_longer: reorganized (AEC, AND, BNZ, CDR, CWT, …) into (site, fruits) [was 65x10, now 585x3]

# A tibble: 585 × 3
    year site  fruits
   <dbl> <chr>  <dbl>
 1  1988 AEC    252. 
 2  1988 AND     27.0
 3  1988 BNZ    198. 
 4  1988 CDR     NA  
 5  1988 CWT     NA  
 6  1988 HBR     NA  
 7  1988 HFR     NA  
 8  1988 LUQ     NA  
 9  1988 SEV     NA  
10  1989 AEC    656. 
# ℹ 575 more rows

Combinar bases de datos

Combinar bases de datos con `join`

Combinar bases de datos

Leemos un nuevo dataset con información de atributos para las especies de árboles:

sp_info <- read_csv(here("data/species_attributes.csv"))

Rows: 104 Columns: 17
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (15): species_name, family, genus, epithet, pollinator_code, mycorrhiza_...
dbl  (2): seed_development_years, seed_mass_mg

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Combinar bases de datos

glimpse(sp_info)

Rows: 104
Columns: 17
$ species_name           <chr> "Abies_amabilis", "Abies_concolor", "Abies_gran…
$ family                 <chr> "Pinaceae", "Pinaceae", "Pinaceae", "Pinaceae",…
$ genus                  <chr> "Abies", "Abies", "Abies", "Abies", "Abies", "A…
$ epithet                <chr> "amabilis", "concolor", "grandis", "lasiocarpa"…
$ seed_development_years <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,…
$ pollinator_code        <chr> "wind", "wind", "wind", "wind", "wind", "wind",…
$ mycorrhiza_type        <chr> "EM", "EM", "EM", "EM", "EM", "EM", "AM", "AM",…
$ needleleaf_broadleaf   <chr> "needleleaf", "needleleaf", "needleleaf", "need…
$ deciduous_evergreen    <chr> "evergreen", "evergreen", "evergreen", "evergre…
$ seed_maturation_timing <chr> "late summer", "fall", "late summer", "late sum…
$ seed_mass_mg           <dbl> 46.2063354, 34.2847056, 21.0800075, 13.7327226,…
$ sexual_system          <chr> "monoecious", "monoecious", "monoecious", "mono…
$ shade_tolerance        <chr> "tolerant", "tolerant", "tolerant", "tolerant",…
$ growth_form            <chr> "tree", "tree", "tree", "tree", "tree", "tree",…
$ seed_bank              <chr> "no", "no", "no", "no", "yes", "yes", "no", "no…
$ fleshy_fruit           <chr> "no", "no", "no", "no", "no", "no", "no", "no",…
$ dispersal_syndrome     <chr> "abiotic", "abiotic", "abiotic", "abiotic", "ab…

Combinar bases de datos

La función count cuenta el número de casos para una variable categórica

sp_info |> count(pollinator_code)

# A tibble: 2 × 2
  pollinator_code     n
  <chr>           <int>
1 animal             73
2 wind               31

sp_info |> count(family)

# A tibble: 41 × 2
   family            n
   <chr>         <int>
 1 Aceraceae         2
 2 Annonaceae        2
 3 Aquifoliaceae     1
 4 Araliaceae        2
 5 Arecaceae         1
 6 Betulaceae        4
 7 Bignoniaceae      2
 8 Boraginaceae      2
 9 Burseraceae       2
10 Cecropiaceae      1
# ℹ 31 more rows

Combinar bases de datos

Usando left_join()

dt_sp <- dt_fix |> 
  left_join(sp_info, by = c("species_name"))

left_join: added 16 columns (family, genus, epithet, seed_development_years, pollinator_code, …)

           > rows only in x      293

           > rows only in y  (     1)

           > matched rows     81,987

           >                 ========

           > rows total       82,280

Combinar bases de datos

setdiff(sp_info$species_name, dt_fix$species_name)

[1] "Myrcia_amazonica"

Combinar bases de datos

glimpse(dt_sp)

Rows: 82,280
Columns: 26
$ site                   <chr> "AND", "AND", "AND", "AND", "AND", "AND", "AND"…
$ year                   <dbl> 1962, 1965, 1967, 1968, 1971, 1974, 1976, 1978,…
$ species_name           <chr> "Abies_amabilis", "Abies_amabilis", "Abies_amab…
$ plant_ID               <chr> "CNCT_01ABAM1", "CNCT_01ABAM1", "CNCT_01ABAM1",…
$ count                  <dbl> 22, 2, 2, 108, 7, 2, 12, 21, 1, 30, 61, 76, 5, …
$ method                 <chr> "PARTIALCONECOUNT", "PARTIALCONECOUNT", "PARTIA…
$ stem_cm                <dbl> 56.6, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ trap_area_m2           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ fruits_per_m2          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ fruits                 <dbl> 22, 2, 2, 108, 7, 2, 12, 21, 1, 30, 61, 76, 5, …
$ family                 <chr> "Pinaceae", "Pinaceae", "Pinaceae", "Pinaceae",…
$ genus                  <chr> "Abies", "Abies", "Abies", "Abies", "Abies", "A…
$ epithet                <chr> "amabilis", "amabilis", "amabilis", "amabilis",…
$ seed_development_years <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,…
$ pollinator_code        <chr> "wind", "wind", "wind", "wind", "wind", "wind",…
$ mycorrhiza_type        <chr> "EM", "EM", "EM", "EM", "EM", "EM", "EM", "EM",…
$ needleleaf_broadleaf   <chr> "needleleaf", "needleleaf", "needleleaf", "need…
$ deciduous_evergreen    <chr> "evergreen", "evergreen", "evergreen", "evergre…
$ seed_maturation_timing <chr> "late summer", "late summer", "late summer", "l…
$ seed_mass_mg           <dbl> 46.20634, 46.20634, 46.20634, 46.20634, 46.2063…
$ sexual_system          <chr> "monoecious", "monoecious", "monoecious", "mono…
$ shade_tolerance        <chr> "tolerant", "tolerant", "tolerant", "tolerant",…
$ growth_form            <chr> "tree", "tree", "tree", "tree", "tree", "tree",…
$ seed_bank              <chr> "no", "no", "no", "no", "no", "no", "no", "no",…
$ fleshy_fruit           <chr> "no", "no", "no", "no", "no", "no", "no", "no",…
$ dispersal_syndrome     <chr> "abiotic", "abiotic", "abiotic", "abiotic", "ab…

Guardar dataset

write_csv(dt_sp, here("data/clean_data.csv"))
#write_csv2(dt_sp, here("data/clean_data.csv"))

write_csv - usa separador de “,”
write_csv2 - usa separador de “;”
write_delim - usa cualquier separador de datos (ej. delim = “|”)

Recursos

Tidyverse packages
R for Data Science Book - Capítulo Wrangle
RStudio CheatSheets
- Data import with readr, readxl, and googlesheets4
- Data Transformation with dplyr
- Data tidying with tidyr
- String manipulation with stringr
- Factors with forcats
- Dates and times with lubridate

Ejercicio 1:

Usando la base de datos final (dt_sp), seleccionar datos con información para diámetro de tronco (stem_cm) y ordernar de mayor a menor:

Ejercicio 1:

Usando la base de datos final (dt_sp), seleccionar datos con información para diámetro de tronco (stem_cm) y ordernar de mayor a menor:

dt_sp |>
  filter(!is.na(stem_cm)) |>
  arrange(desc(stem_cm))

filter: removed 80,777 rows (98%), 1,503 rows remaining

# A tibble: 1,503 × 26
   site   year species_name  plant_ID      count method     stem_cm trap_area_m2
   <chr> <dbl> <chr>         <chr>         <dbl> <chr>        <dbl>        <dbl>
 1 AND    1993 Abies_procera CNCT_37ABPR17   250 PARTIALCO…    221.           NA
 2 AND    1962 Abies_procera CNCT_15ABPR15    50 PARTIALCO…    198.           NA
 3 AND    1962 Abies_procera CNCT_15ABPR8     68 PARTIALCO…    196.           NA
 4 AND    1993 Abies_procera CNCT_37ABPR1    350 PARTIALCO…    186.           NA
 5 AND    1961 Abies_procera CNCT_37ABPR3    231 PARTIALCO…    186.           NA
 6 AND    1993 Abies_procera CNCT_37ABPR3    410 PARTIALCO…    184.           NA
 7 AND    1962 Abies_procera CNCT_15ABPR16   130 PARTIALCO…    183.           NA
 8 AND    1992 Abies_procera CNCT_02ABPR35    54 PARTIALCO…    180.           NA
 9 AND    1962 Abies_procera CNCT_15ABPR3    300 PARTIALCO…    178.           NA
10 AND    1993 Abies_procera CNCT_37ABPR10   250 PARTIALCO…    174.           NA
# ℹ 1,493 more rows
# ℹ 18 more variables: fruits_per_m2 <dbl>, fruits <dbl>, family <chr>,
#   genus <chr>, epithet <chr>, seed_development_years <dbl>,
#   pollinator_code <chr>, mycorrhiza_type <chr>, needleleaf_broadleaf <chr>,
#   deciduous_evergreen <chr>, seed_maturation_timing <chr>,
#   seed_mass_mg <dbl>, sexual_system <chr>, shade_tolerance <chr>,
#   growth_form <chr>, seed_bank <chr>, fleshy_fruit <chr>, …

Ejercicio 2:

Usando la base de datos final (dt_sp), calcular diámetro medio y SD para cada especie de árbol.

Ejercicio 2:

Usando la base de datos final (dt_sp), calcular diámetro medio y SD para cada especie de árbol.

dt_sp |>
  filter(!is.na(stem_cm)) |>
  group_by(species_name) |> 
  summarise(mean = mean(stem_cm),
            sd = sd(stem_cm))

filter: removed 80,777 rows (98%), 1,503 rows remaining

group_by: one grouping variable (species_name)

summarise: now 10 rows and 3 columns, ungrouped

# A tibble: 10 × 3
   species_name       mean    sd
   <chr>             <dbl> <dbl>
 1 Abies_amabilis     65.0  19.7
 2 Abies_concolor     63.1  18.6
 3 Abies_grandis      74.5  14.8
 4 Abies_lasiocarpa   45.0  16.2
 5 Abies_magnifica    87.9  19.9
 6 Abies_procera     104.   34.4
 7 Picea_engelmannii  80.2  16.5
 8 Pinus_lambertiana 114.   27.7
 9 Pinus_monticola    63.4  22.4
10 Tsuga_mertensiana  56.5  12.5

Ejercicio 3:

Usando la base de datos final (dt_sp), calcular el número de árboles y número de especies mayores de 40cm de diámetro y menores de 40cm de diámetro.

Ejercicio 3:

Usando la base de datos final (dt_sp), calcular el número de árboles y número de especies mayores de 40cm de diámetro y menores de 40cm de diámetro.

dt |>
  filter(!is.na(stem_cm)) |>
  mutate(tree_size = case_when(stem_cm >= 40 ~ "big",
                               stem_cm < 40 ~ "small")) |> 
  group_by(tree_size) |> 
  summarise(n_trees = n(), 
            n_species = n_distinct(species_name))

filter: removed 210,780 rows (99%), 2,282 rows remaining

mutate: new variable 'tree_size' (character) with 2 unique values and 0% NA

group_by: one grouping variable (tree_size)

summarise: now 2 rows and 3 columns, ungrouped

# A tibble: 2 × 3
  tree_size n_trees n_species
  <chr>       <int>     <int>
1 big          2144        10
2 small         138         6

Ejercicio 4:

Usando la base de datos final (dt_sp), seleccionar sitios con método de conteo tipo “TRAP” y calcular cantidad máxima y mínima de frutos por m2 para cada sitio.

Ejercicio 4:

Usando la base de datos final (dt_sp), seleccionar sitios con método de conteo tipo “TRAP” y calcular cantidad máxima y mínima de frutos por m2 para cada sitio.

dt_sp |>
  filter(method == "TRAP") |> 
  group_by(site) |>
  summarise(max_fruit = max(fruits_per_m2),
            min_fruit = mean(fruits_per_m2))

filter: removed 33,191 rows (40%), 49,089 rows remaining

group_by: one grouping variable (site)

summarise: now 5 rows and 3 columns, ungrouped

# A tibble: 5 × 3
  site  max_fruit min_fruit
  <chr>     <dbl>     <dbl>
1 AEC       8107.     296. 
2 BNZ      28920     1088. 
3 CWT      12207.     197. 
4 HBR       2440       93.4
5 LUQ     213300      299.

Ejercicio 5:

Usando la base de datos final (dt_sp), crear una tabla que compare la suma de frutos contados en los sitios CWT y HFR (en columnas), para los años entre 2000-2010 (filas).

Ejercicio 5:

Usando la base de datos final (dt_sp), crear una tabla que compare la suma de frutos contados en los sitios CWT y HFR (en columnas), para los años entre 2000-2010 (filas).

dt_sp |>
  filter(site %in% c("CWT", "SEV")) |>
  filter(year %in% c(2000:2010)) |>
  group_by(site, year) |>
  summarise(fruits = sum(fruits)) |>
  pivot_wider(names_from = site, values_from = fruits)

# A tibble: 11 × 3
    year     CWT    SEV
   <dbl>   <dbl>  <dbl>
 1  2000  92776.  5561.
 2  2001 133160. 28243.
 3  2002  45746.   302.
 4  2003  63213. 13646.
 5  2004  67092. 23964.
 6  2005  47034. 20558.
 7  2006  84603.   726.
 8  2007 114387. 11630.
 9  2008 147617. 14634 
10  2009 138707.     6 
11  2010 139444. 15441.

Ejercicio 6:

Usando la base de datos final (dt_sp), crear una tabla que compare la suma de frutos contados entre los años 2001 y 2005 (en columnas), para las especies de Abies (filas).

Ejercicio 6:

Usando la base de datos final (dt_sp), crear una tabla que compare la suma de frutos contados entre los años 2001 y 2005 (en columnas), para las especies de Abies (filas).

dt_sp |>
  filter(year %in% c(2001:2005)) |>
  filter(str_detect(species_name, "Abies")) |>
  group_by(year, species_name) |>
  summarise(fruits = sum(fruits)) |>
  pivot_wider(names_from = year, values_from = fruits)

# A tibble: 6 × 6
  species_name     `2001` `2002` `2003` `2004` `2005`
  <chr>             <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
1 Abies_amabilis      721   2819   5907     54    864
2 Abies_concolor     1429     92   3032     NA    136
3 Abies_grandis      3509    238   4414     17   1119
4 Abies_magnifica      52   1374   6324      8    570
5 Abies_procera      3308   7772  10485    957   1588
6 Abies_lasiocarpa     NA    443   1974     17     73

Manejo de datos en R

Carpeta con material

Exploración de datos

Formato tidy data

Paquetes que usaremos

Paquetes incluidos en tidyverse

Otros paquetes útiles para el manejo de datos

Importar datos

Ruta a los datos

El operador ‘pipe’

Dataset

Dataset

Cargar paquetes

Importar datos

Funciones de dplyr (parte 1)

Ordernar datos por columnas

Ordernar datos por columnas

Ordernar datos por columnas

Renombrar variables

Organizar columnas

Seleccionar variables de interés

Seleccionar variables de interés

Seleccionar variables de interés

Seleccionar variables de interés

Resumen datos

Resumen datos

Resumen datos

Resumen datos

Funciones de dplyr (parte 2):

Extraer valores únicos

Extraer valores únicos

Extraer valores únicos

Crear nuevas variables

Filtrar datos

Filtrar datos

Agrupar datos y resumir

Agrupar datos y resumir

Agrupar datos y resumir

Agrupar datos y resumir

Crear categorias

Crear categorias

Crear categorias

Funciones vistas de dplyr

Funciones de dplyr (parte 1 y 2)

Corregir datos

Modificar datos

Modificar datos

Reestructurar datos

Reestructurar datos con library(tidyr)

Reestructurar datos

Reestructurar datos

Reestructurar datos

Reestructurar datos

Combinar bases de datos

Combinar bases de datos con join

Combinar bases de datos

Combinar bases de datos

Combinar bases de datos

Combinar bases de datos

Combinar bases de datos

Combinar bases de datos

Guardar dataset

Recursos

Ejercicio 1:

Ejercicio 1:

Ejercicio 2:

Ejercicio 2:

Ejercicio 3:

Ejercicio 3:

Ejercicio 4:

Ejercicio 4:

Ejercicio 5:

Ejercicio 5:

Ejercicio 6:

Ejercicio 6:

Reestructurar datos con `library(tidyr)`

Combinar bases de datos con `join`